Funded by Centers for Disease Control and Prevention (CDC)/National Institute for Occupational Safety and Health (NIOSH)
Reducing Struck-By Injuries Through Behavior Research and Safety Innovation
🛠 Why This Project Matters
Tow truck operators face one of the most dangerous jobs in America—with a fatality rate over 15 times the national average. Each year, dozens of workers are killed in struck-by incidents, when passing vehicles fail to slow down or move over while workers are assisting motorists on the roadside.
Funded by the Centers for Disease Control and Prevention (CDC) and the National Institute for Occupational Safety and Health (NIOSH), this research project aims to reduce injuries and fatalities by identifying key risk factors and developing data-driven, actionable safety strategies.
🎯 Project Goals
Our mission is to better understand how tow truck operators and drivers behave in real roadside environments—and use that knowledge to design safer systems and policies.
Aim 1: Legal Framework and Enforcement
- Review “Slow Down, Move Over” laws across all 50 states
- Analyze safety rules and regulations for roadside workers
- Interview law enforcement officers nationwide about how these laws are enforced
Aim 2: Real-World Risks Faced by Tow Truck Workers
- Conduct national surveys and in-depth interviews with tow truck operators
- Observe real roadside service scenes to identify hazardous patterns
- Investigate the human, environmental, and procedural factors that increase risk
Aim 3: Understanding Driver Behavior
- Survey drivers to assess awareness of tow truck safety laws
- Use driving simulators to study how environmental factors (lighting, signage, vehicle placement) influence driver decisions and reaction times
🤖 Role of the AI Center: Understanding Driver Decision-Making
As part of Aim 2, the Alabama Center for the Advancement of Artificial Intelligence (ALAAI) will lead efforts to analyze physiological and behavioral signals from drivers, including:
- Eye-tracking and attention monitoring
- Stress, fatigue, and cognitive load analysis through biometric data
- Modeling driver decision-making under different roadside risk conditions
This work will inform the development of intelligent systems and evidence-based training tools to enhance roadside awareness and reduce struck-by incidents.
🌐 Broader Impact
This research will contribute to:
- Policy recommendations for better law enforcement and worker protection
- Public awareness campaigns targeting driver behavior
- Technology-informed training and equipment guidelines for tow truck operators
- New AI-enhanced safety insights that can benefit roadside workers in various sectors, including emergency responders and utility crews